AlterLab Polars
Skill AktivPart of the AlterLab Academic Skills suite. Fast in-memory DataFrame library for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory. Lazy evaluation, parallel execution, Apache Arrow backend. Best for 1-100GB datasets, ETL pipelines, faster pandas replacement. For larger-than-RAM data use dask or vaex.
To offer a significantly faster in-memory DataFrame library for datasets that fit within RAM, serving as a high-performance alternative to pandas for ETL pipelines and data analysis.
Funktionen
- Fast in-memory DataFrame processing
- Lazy evaluation for query optimization
- Parallel execution for multi-core performance
- Apache Arrow backend for efficient data handling
- Expressive API for complex data transformations
Anwendungsfälle
- Replacing slow pandas operations for datasets fitting in RAM
- Optimizing ETL pipelines for faster data processing
- Performing complex data analysis and transformations efficiently
- Working with datasets in the 1-100GB range
Nicht-Ziele
- Handling datasets larger than available RAM (use dask or vaex)
- Replacing specialized database connectors
- Providing a full-fledged analytics platform beyond DataFrame operations
Trust
- warning:Issues AttentionThere are 2 open issues and 0 closed issues in the last 90 days, indicating a very low closure rate and slow response to new issues.
Installation
npx skills add AlterLab-IEU/AlterLab-Academic-SkillsFührt das Vercel skills CLI (skills.sh) via npx aus — benötigt Node.js lokal und mindestens einen installierten skills-kompatiblen Agent (Claude Code, Cursor, Codex, …). Setzt voraus, dass das Repo dem agentskills.io-Format folgt.
Qualitätspunktzahl
Vertrauenssignale
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